Modeling human genetic variation is critical to understanding the genetic basis of complex disease. The Human Genome Project has discovered millions of DNA sequence variants (single nucleotide polymorphisms or SNPs), and millions more may exist. As the coding for proteins takes place along chromosomes, SNP organization along each chromosome, the haplotype structure, will be most useful for discovering genetic variants associated with disease. Haplotype-based association studies are powerful procedures for detecting genetic influences on complex diseases. However, association tests of haplotype effects with unphased genotype data can be sensitive to estimates of haplotype frequencies even with family-based study designs and complete genotype information. The broad objectives of this proposal focus on enhancing the arsenal of statistical methods researchers use to dissect genetic factors in complex diseases. Specifically, we propose to apply results from coarsened-data semi-parametric efficient model theory to derive optimal tests and estimates of haplotype and haplotype interaction effects that are robust to haplotype frequencies using unphased, and possibly missing, genotype data. The data structures we will consider are motivated by those found in the Genetics of Early Onset Cardiovascular Disease (GENECARD) study and the GENECARD Offspring Study. In addition, we propose to apply these newly developed techniques to the GENECARD samples in fine mapping and candidate gene studies. This research will form the core of a 5-year career development plan for Dr. Andrew Allen under the mentorship of three exceptional researchers, each with expertise that complements one another and represent the three areas addressed in this proposal: cardiology, genetics, and statistics. They propose a career development plan that combines didactic and practical training in genetics, cardiology, and genetic epidemiology with an ongoing research program within the unique research environment of Duke University. This career development plan will foster Dr. Allen's development into an established independent quantitative research scientist with expertise in both methodology for dissecting genetic factors in complex disease and cardiovascular genetics.